Artificial Intelligence-Driven Video Indexing for Rapid Surveillance Footage Summarization and Review
Jaemin Jung, Soonyong Park, Harim Kim, Changha Lee, Charmgil Hong
Abstract
This paper introduces VIDEX, an advanced tool designed to streamline the analysis of surveillance video through a user-friendly interface. VIDEX achieves high development efficiency and maintainability utilizing the Model-View-ViewModel (MVVM) design pattern. The core feature of VIDEX is a footage summary using object detection and anomaly detection. Its architecture ensures efficient data management by organizing detected objects and anomalies within an indexed database, thus facilitating a more rapid review process. Additionally, multi-threading was used to shorten the processing time. VIDEX provides a video summarization that can be used primarily in the criminal investigation stage using the information stored in a database. Discover more about VIDEX and access its resources at https://github.com/nth221/videx.
BibTeX
@inproceedings{ijcai2024p1009,
title = {Artificial Intelligence-Driven Video Indexing for Rapid Surveillance Footage Summarization and Review},
author = {Jung, Jaemin and Park, Soonyong and Kim, Harim and Lee, Changha and Hong, Charmgil},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8687--8690},
year = {2024},
month = {8},
note = {Demo Track},
doi = {10.24963/ijcai.2024/1009},
url = {https://doi.org/10.24963/ijcai.2024/1009},
}